Sensing of indoor air quality—characterization of spatial and temporal pollutant evolution through distributed sensing

Discouraged by the high-cost and lack of connectivity of indoor air quality (iAQ) measurement equipment, we built a platform that would allow us to investigate what kinds of iAQ evolution information could be collected by a low-cost, distributed sensor network. Our platform measures a variety of iAQ metrics (CO ₂ , HCHO, volatile organic compounds, NO ₂ , O ₃ , temperature, and relative humidity), can be flexibly powered by batteries or standard 5 W power supplies, and is connected to an infrastructure that supports an arbitrary number of nodes that push data to the cloud and record it in real-time. Some of the sensors used in our nodes generate data in standard units (like ppm or °C), and others provide an analog signal that cannot be directly converted into standard units. To increase the relative precision of measurements taken by different nodes, we placed all 6 pairs of the nodes used in our deployments in the same environment, recorded how they reacted to changing iAQ, and developed calibration functions to synchronize their signals. We deployed the comparatively cross-calibrated nodes to two different buildings on Princeton University's campus; a fabrication shop and an office building. In both buildings, we placed nodes at key positions in the ventilation supply chain, providing us with the ability to monitor where indoor air pollutants were being introduced, and when they tended to be introduced—enabling us to monitor the evolution of pollutants temporally and spatially. We find that the occupied space of the first building's fabrication shop and the second building's open-plan office have higher levels of volatile organic compounds (VOCs) than outside air. This indicates that both buildings' ventilation systems are unable to supply enough fresh air to dilute VOCs generated inside those spaces. In the second building, we also find indications that other parameters are being driven by set-backs and occupancy. These first deployments demonstrate the ability of low-cost distributed iAQ sensor networks to help researchers identify where and when indoor air pollutants are introduced in buildings.